An important function of a radar system, whether a Real Beam type, Synthetic Aperture (SAR) or Interferometric SAR is to detect a target as well as identify it. An example of a SAR image is shown in FIG. 1. Radar target detection and identification have been proven necessary in military surveillance, reconnaissance, and combat missions. The detection and identification of targets provide real-time assessment of the number and the locations of targets of interest.
One exemplary method of target detection and identification is to process the image acquired by the radar using Synthetic Aperture Radar (SAR) technology. By processing a SAR generated image, targets can be identified and the features of a target can be extracted and matched to a database for identification.
The general principle behind SAR is to obtain high-resolution images by coherently combining the amplitude and phase information of separate radar returns from a plurality of sequentially transmitted pulses from a relatively small antenna on a moving platform. The returns from the plurality of pulses transmitted during a SAR image, when coherently combined and processed, result in image quality comparable to a longer antenna, corresponding approximately to the synthetic “length” traveled by the antenna during the acquisition of the image.
Attempts have been made toward target identification extracted from SAR radar images. A commonly used technique to find man-made objects/targets in a SAR image is constant false alarm rate (CFAR), which is described in D. E. Kreithen, S. D. Halverson and G. J. Owirka's “Discriminating Targets from Clutter,” Lincoln Lab, vol. 6, no. 1, 1993; and L. M. Novak, G. J. Owirka and C. M. Netishen's “Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System,” Lincoln Lab, vol. 6, no. 1, 1993. However tactical targets can be found operating in many different terrains, namely areas with substantial tree forest coverage, making target identification using only CFAR difficult. Further, targets can also use the tree line to avoid detection from radar, such as shown in FIG. 1. The presence of the tree line adds clutter to the SAR image, changing the contour, obfuscating the rendition of a target in the SAR image and thereby significantly reducing the ability of automated processing techniques to detect and identify targets. When the existing CFAR is used in these situations, it can confuse trees for targets, causing false alarms and often overwhelming downstream targets.
Some attempts have been made to address these issues, reduce false alarms and improve target identification. For example, Charles H. Fosgate, and Hamid Krim in Multiscale Segmentation and Anomaly Enhancement of SAR Imagery, IEEE Transactions on Image Processing, Vol. 6, No. 1, January 1997, use complex algorithms associated with multiscale stochastic models to segment distinct cluster types, such as forest and grass, in order to facilitate target identification. The Coherent Multi-Scale (CMS) algorithm used in this article requires the phase information of the SAR image analyzed.
In another example, N. Khatib and A. Ezekiel in “SAR ATR Treeline Extended Operating Condition”. U.S. Pat. No. 7,787,657, filed Mar. 9, 2009, attempt to mitigate natural clutter by comparing one or more historically known target characteristics and one or more measured characteristics to obtain an output, identifying boundaries for one or more objects within the output and suppressing clutter pixels that are identified external the one or more objects.
In another example, T. J. Peregrim, F. A. Okurowski and A. H. Long in “Synthetic aperture radar guidance system and method of operating same”. U.S. Pat. No. 5,430,445, filed Dec. 31, 1992, compares each pixel of a SAR image with a target template until a match is provided.
Despite these attempts, high false alarm rates and clutter in the SAR image continue to cause problems in target identification.